AI Algorithms

Algorithms are a constant presence in our day-to-day lives. They are responsible for optimising our experiences in daily tasks such as searching for content on social networks, or consulting a chatbot programmed to respond immediately to our requests. But to fully understand how they work, it is necessary to do more research on artificial intelligence and learn about the different types of AI algorithms.

As many already know, AI applications aim to train machines so that they can operate autonomously with the same skills as a human being. But what few know is how this system works: it is all about a combination of algorithms that together are able to perform multiple activities quickly and efficiently. Based on machine learning, these algorithms are a series of steps that must be followed in a certain order to accomplish a task.

Among these tasks, we can find: faster medical diagnoses for the early detection of a disease, personalised purchase suggestions on digital platforms, virtual assistants such as Alexa or Siri and even simultaneous translations. Artificial intelligence algorithms play a key role for digital transformation – providing a wide range of applications that make our daily lives easier, and having, on top of that, an incredible ability to analyse large volumes of data and detect similarities that could go unnoticed by the human eye.

These advances in artificial intelligence – a concept with an almost century-old history – are transforming our interaction with technology and redefining the way we work, interact or enjoy our leisure time. However, for these powerful tools to be effective, we need to know their true capabilities. Below we will explain the different types of learning algorithms in artificial intelligence.

Reinforcement Learning Algorithms

Reinforcement learning algorithms represent a technique that focuses on the decision-making based on one's own experience. Instead of relying on labelled data, like other types of algorithms, this category is based on a trial-and-error training process, in which the machine receives rewards or punishments that serve to improve its action strategy. In this sense, here are some examples:

Autonomous robotics

Autonomous robots use reinforcement learning algorithms to learn to navigate in unfamiliar environments, manipulate objects and perform complex tasks.

Resource management and planning

These algorithms are also widely applied in environments such as traffic guidance, financial portfolio optimisation and energy management, with the aim of maximising efficiency and resources.

Virtual assistants

Algorithms are applied in virtual assistants and chatbots to understand user preferences and provide more personalised responses, as well as in the automation of customer service or technical support tasks.

Computer vision

Algorithms are essential in facial recognition, motion tracking and image analysis, which is used in applications embedded in security systems or self-driving vehicles.

Medical diagnostics

Algorithms assist healthcare professionals in the identification of diseases through the analysis of medical images such as X-rays and MRI scans.

 SEE INFOGRAPHIC: Discover the benefits of Artificial Intelligence Algorithms [PDF]

Supervised Learning Algorithms

This type of algorithm uses a set of training data that has been previously labelled, i.e. data that has been assigned one or more categories to add context or meaning. In the field of machine learning, this data serves as a basis for making predictions or more accurate decisions about new data not included in the trained set.

The main goal of Supervised Learning algorithms is to learn a function or model capable of accurately mapping inputs to desired outputs. This technique is used in a wide range of applications. The following are the best known:

Text and natural language classification

Supervised learning algorithms are used in natural language processing applications such as spam filtering and sentiment analysis in social networks.

Recommendation system

These algorithms are applied in streaming or e-commerce platforms to make content recommendations, suggesting products, movies or music based on the user's history and preferences.

Unsupervised Learning Algorithms

Unsupervised learning algorithms are used to discover hidden patterns in unlabelled data. Supervised Learning algorithms have prior knowledge of the desired answers – whereas Unsupervised Learning algorithms do not have an ordered dataset. They determine by themselves which are the most important common features of a dataset and then group them according to their similarities. 

These algorithms can identify audience segments with similar interests and have a wide range of applications. For example:

Clustering

Algorithms are used to group similar data into clusters, which is essential for market segmentation analysis and news categorisation.

Anomaly detection

Unsupervised learning algorithms are useful in detecting unusual behaviour or anomalies in data, which is applied in cyber security and financial fraud.

Deep Learning Algorithms

Deep Learning is a specialised branch of artificial intelligence that focuses on the construction and training of deep neural networks. These networks – inspired by the structure and function of the human brain – are structured as layers of interconnected nodes; each performing complex mathematical operations. In contrast to more traditional models, deep neural networks are capable of learning hierarchical representations and abstractions of data, which makes them exceptionally effective for tasks such as pattern recognition, natural language processing, computer vision, etc. 

Deep Learning algorithms have a multitude of applications, with an important role in fields where computer vision, virtual assistants and chatbots are used. In the field of robotics, it facilitates autonomous learning to improve the ability to navigate, manipulate targets and adapt to changing environments. In business, predictive analytics models complex patterns to predict future events, such as market trends and consumer behaviour. Its ability to analyse big data helps strategic decision-making in fields such as finance and logistics.

Natural Language Processing (NLP) Algorithms

Natural Language Processing (NLP) algorithms are a specialised category of artificial intelligence algorithms designed to understand and process human language efficiently. They have the ability to enable machines to interpret, analyse and generate text in a manner similar to that of a human being, which allows a more natural communication between humans and machines. Today, this field of computer science has a multitude of applications such as:

Sentiment analysis

They assess the emotional tone of texts, comments or reviews, which is important information for companies wanting to understand the public's perception of products or services.

Document classification

they automatically categorise documents according to subject matter or style, streamlining the management of large amounts of data in business and government environments.

Machine translation

They facilitate machine translation between different languages and are essential for globalisation and effective communication in an increasingly interconnected world.

Iberdrola and the types of AI algorithms

Artificial intelligence (AI) is a technology that is already part of Iberdrola's business, and its different applications have become an essential resource for the company. Below, we review some of our initiatives using artificial intelligence algorithms.